White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and ...patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.
We evaluated sex differences in MRI-based volume loss and differences in predictors of this neurodegeneration in cognitively healthy older adults. Mixed-effects regression was used to compare ...regional brain volume trajectories of 295 male and 328 female cognitively healthy Baltimore Longitudinal Study of Aging participants, aged 55–92 years, with up to 20 years of follow-up and to assess sex differences in the associations of age, hypertension, obesity, APOE e4 carrier status, and high-density lipoprotein cholesterol with regional brain volume trajectories. For both sexes, older age was associated with steeper volumetric declines in many brain regions, with sex differences in volume loss observed in frontal, temporal, and parietal regions. In males, hypertension and higher high-density lipoprotein cholesterol were protective against volume loss in the hippocampus, entorhinal cortex, and parahippocampal gyrus. In females, hypertension was associated with steeper volumetric decline in gray matter, and obesity was protective against volume loss in temporal gray matter. Predictors of volume change may affect annual rates of volume change differently between men and women.
•Older men show greater volume loss over time than older women.•Cardiovascular risk factors affect rates of volume change differently by sex.•Hypertension and high-density lipoprotein cholesterol are related to less steep volume declines in men.•Hypertension is related to steeper volume decline in gray matter in women.•Obesity is related to less steep volume decline in temporal gray matter in women.
As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to ...variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.
•A new method for consistent parcellation of brain anatomy in hetero-geneous datasets.•We create protocol/site specific atlas pairs for harmonization of seg-mentations.•Atlas pairs preserve original scan characteristics while sharing common anatomical labels.•Final parcellation is performed via multi-atlas segmentation using harmonized atlases.
IMPORTANCE: There are currently no proven treatments to reduce the risk of mild cognitive impairment and dementia. OBJECTIVE: To evaluate the effect of intensive blood pressure control on risk of ...dementia. DESIGN, SETTING, AND PARTICIPANTS: Randomized clinical trial conducted at 102 sites in the United States and Puerto Rico among adults aged 50 years or older with hypertension but without diabetes or history of stroke. Randomization began on November 8, 2010. The trial was stopped early for benefit on its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. The final date for follow-up of cognitive outcomes was July 22, 2018. INTERVENTIONS: Participants were randomized to a systolic blood pressure goal of either less than 120 mm Hg (intensive treatment group; n = 4678) or less than 140 mm Hg (standard treatment group; n = 4683). MAIN OUTCOMES AND MEASURES: The primary cognitive outcome was occurrence of adjudicated probable dementia. Secondary cognitive outcomes included adjudicated mild cognitive impairment and a composite outcome of mild cognitive impairment or probable dementia. RESULTS: Among 9361 randomized participants (mean age, 67.9 years; 3332 women 35.6%), 8563 (91.5%) completed at least 1 follow-up cognitive assessment. The median intervention period was 3.34 years. During a total median follow-up of 5.11 years, adjudicated probable dementia occurred in 149 participants in the intensive treatment group vs 176 in the standard treatment group (7.2 vs 8.6 cases per 1000 person-years; hazard ratio HR, 0.83; 95% CI, 0.67-1.04). Intensive BP control significantly reduced the risk of mild cognitive impairment (14.6 vs 18.3 cases per 1000 person-years; HR, 0.81; 95% CI, 0.69-0.95) and the combined rate of mild cognitive impairment or probable dementia (20.2 vs 24.1 cases per 1000 person-years; HR, 0.85; 95% CI, 0.74-0.97). CONCLUSIONS AND RELEVANCE: Among ambulatory adults with hypertension, treating to a systolic blood pressure goal of less than 120 mm Hg compared with a goal of less than 140 mm Hg did not result in a significant reduction in the risk of probable dementia. Because of early study termination and fewer than expected cases of dementia, the study may have been underpowered for this end point. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01206062
We present a brain development index (BDI) that concisely summarizes complex imaging patterns of structural brain maturation along a single dimension using a machine learning methodology. The brain ...was found to follow a remarkably consistent developmental trajectory in a sample of 621 subjects of ages 8-22 participating in the Philadelphia Neurodevelopmental Cohort, reflected by a cross-validated correlation coefficient between chronologic age and the BDI of r = 0.89. Critically, deviations from this trajectory related to cognitive performance. Specifically, subjects whose BDI was higher than their chronological age displayed significantly superior cognitive processing speed compared with subjects whose BDI was lower than their actual age. These results indicate that the multiparametric imaging patterns summarized by the BDI can accurately delineate trajectories of brain development and identify individuals with cognitive precocity or delay.
Background
Ideal cardiovascular health has previously been associated with a lower risk of dementia and less cognitive decline in midlife. We investigated if the American Heart Association’s (AHA) ...“life’s essential 8” (LE8) factors for ideal cardiovascular health are related to brain structure integrity in midlife.
Methods
We included 607 participants from the Coronary Artery Risk Development in Young Adults study with information on LE8 at mean age of 45 (SD = 3.4) and MRI examination ten years later. LE8 factors were dichotomized according to AHA guidelines as yes/no on high blood pressure (≥120 systolic or ≥80 diastolic blood pressure); high BMI (≥25); low physical activity (≤300 exercise units); currently smoking; high cholesterol (≥200); high fasting glucose (≥100); poor diet (<median score on the MedDiet); and poor sleep (≤6 or ≥9 hours per night, and combined into a score ranging from 0 to 8, with higher score indicating worse cardiovascular health. A brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age‐related atrophy. White matter hyperintesities (WMHs) and white matter (WM) microstructural integrity (mean diffusivity MD and fractional anisotropy FA) were assessed using MRI. Linear regression and structural equation modeling were used to examine the relationships between LE8, brain age, WMHs, FA, and MD, adjusted for age, sex, education, and race.
Results
The range of LE8 was 0‐7 among the participants (median = 2, interquartile range = 2). Having 0‐3 LE8 risk factors was associated with a younger brain age, while having 4+ risk factors was associated with a higher brain age relative to chronological age (Figure 1). Moreover, a higher LE8 score was associated with greater MD (β: 0.16, 95% CI: 0.09 to 0.23), and lower FA (β: ‐0.15, 95% CI: ‐0.22 to ‐0.08), but not with WMHs. The indirect effect of the LE8 score on brain age through MD and FA was (β: 0.50, 95% CI: 0.28 to 0.75), hence the proportion mediated was 75% (Figure 2).
Conclusion
A higher cardiovascular risk as defined by the AHA is associated with a higher brain age in midlife, a majority of which can be explained by markers of early WM deterioration.
Background
Evidence in older adults suggests that depressive symptoms may be part of prodromal mood changes in dementia, rather than a risk factor. However, it is unclear whether variations in ...depressive symptoms in adulthood exhibit distinct characteristics in brain structure and cognitive function in midlife.
Methods
From the Coronary Artery Risk Development in Young Adults study, we identified 662 Black and White participants (age 23‐36 at baseline) who completed the Center for Epidemiological Studies Depression scale (CES‐D) at six time points over 20 years. 20 years after baseline, the participants underwent magnetic resonance imaging to characterize gray matter (GM) structures (total GM, temporal cortex, hippocampus, and entorhinal cortex) and white matter hyperintensities (WMHs). Participants also completed neuropsychological tests including the Digit Symbol Substitution Test (DSST), Rey‐Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment (MoCA), and category and letter fluency tests, analyzed as z‐scores.
Results
Using growth mixture modeling, we identified four trajectories of depressive symptoms (Figure 1): consistently low scorers (“steady low”; n = 509, 76.9%), a class with an early peak and decline in symptoms (“declining”; n = 63, 9.5%), a class with late increases in symptoms (“increasing”; n = 49, 7.4%), and consistently high scorers (“steady high”; n = 41, 6.2%). Compared to the steady low class, the steady high class had lower entorhinal cortex volume (β: ‐180.80, 95% CI: ‐336.69 to ‐24.91). The increasing class, had more WMHs (β: 0.55, 95% CI: 0.22 to 0.89) and less total brain volume (β: ‐9269.25, 95% CI: ‐17104.67 to ‐1444.82). Both the steady high and the increasing classes had poorer performance on the Stroop task (β: ‐0.40, 95% CI: ‐0.70 to ‐0.10; β: ‐0.39, 95% CI: ‐0.64 to ‐0.14, respectively), however the steady high also had poorer DSST performance (β: ‐0.40, 95% CI: ‐0.67 to ‐0.13), while the increasing class had poorer performance on the MoCA (β: ‐1.20, 95% CI: ‐2.04 to ‐0.37). The declining group was not significantly different from the steady low group on any brain or cognitive measures.
Conclusions
Our findings suggest that trajectories in depressive symptoms in young to mid‐adulthood show different cognitive and brain phenotypes in midlife.
Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in ...cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P < 0.001), but was more accurate using functional connectivity data (71% accuracy; P < 0.001). Moreover, the degree to which a given participant's cognitive profile was "male" or "female" was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10(-7)). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity.
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and ...complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.
IMPORTANCE: The effect of intensive blood pressure lowering on brain health remains uncertain. OBJECTIVE: To evaluate the association of intensive blood pressure treatment with cerebral white matter ...lesion and brain volumes. DESIGN, SETTING, AND PARTICIPANTS: A substudy of a multicenter randomized clinical trial of hypertensive adults 50 years or older without a history of diabetes or stroke at 27 sites in the United States. Randomization began on November 8, 2010. The overall trial was stopped early because of benefit for its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. Brain magnetic resonance imaging (MRI) was performed on a subset of participants at baseline (n = 670) and at 4 years of follow-up (n = 449); final follow-up date was July 1, 2016. INTERVENTIONS: Participants were randomized to a systolic blood pressure (SBP) goal of either less than 120 mm Hg (intensive treatment, n = 355) or less than 140 mm Hg (standard treatment, n = 315). MAIN OUTCOMES AND MEASURES: The primary outcome was change in total white matter lesion volume from baseline. Change in total brain volume was a secondary outcome. RESULTS: Among 670 recruited patients who had baseline MRI (mean age, 67.3 SD, 8.2 years; 40.4% women), 449 (67.0%) completed the follow-up MRI at a median of 3.97 years after randomization, after a median intervention period of 3.40 years. In the intensive treatment group, based on a robust linear mixed model, mean white matter lesion volume increased from 4.57 to 5.49 cm3 (difference, 0.92 cm3 95% CI, 0.69 to 1.14) vs an increase from 4.40 to 5.85 cm3 (difference, 1.45 cm3 95% CI, 1.21 to 1.70) in the standard treatment group (between-group difference in change, −0.54 cm3 95% CI, −0.87 to −0.20). Mean total brain volume decreased from 1134.5 to 1104.0 cm3 (difference, −30.6 cm3 95% CI, −32.3 to −28.8) in the intensive treatment group vs a decrease from 1134.0 to 1107.1 cm3 (difference, −26.9 cm3 95% CI, 24.8 to 28.8) in the standard treatment group (between-group difference in change, −3.7 cm3 95% CI, −6.3 to −1.1). CONCLUSIONS AND RELEVANCE: Among hypertensive adults, targeting an SBP of less than 120 mm Hg, compared with less than 140 mm Hg, was significantly associated with a smaller increase in cerebral white matter lesion volume and a greater decrease in total brain volume, although the differences were small. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01206062
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